How to merge a Series and DataFrame
dataframe, pandas, python
Solution
You could construct a dataframe from the series and then merge with the dataframe. So you specify the data as the values but multiply them by the length, set the columns to the index and set params for `left_index` and `right_index` to True:
In [27]:
df.merge(pd.DataFrame(data = [s.values] * len(s), columns = s.index), left_index=True, right_index=True)
Out[27]:
a b s1 s2
0 1 3 5 6
1 2 4 5 6
EDIT for the situation where you want the index of your constructed df from the series to use the index of the df then you can do the following:
df.merge(pd.DataFrame(data = [s.values] * len(df), columns = s.index, index=df.index), left_index=True, right_index=True)
This assumes that the indices match the length.
Problem
If you came here looking for information on how to merge a `DataFrame` and `Series` on the index, please look at this answer. The OP's original intention was to ask how to assign series elements as columns to another DataFrame. If you are interested in knowing the answer to this, look at the accepted answer by EdChum. Best I can come up with is ``` df = pd.DataFrame({'a':[1, 2], 'b':[3, 4]}) # see EDIT below s = pd.Series({'s1':5, 's2':6}) for name in s.index: df[name] = s[name] a b s1 s2 0 1 3 5 6 1 2 4 5 6 ``` Can anybody suggest better syntax / faster method? My attempts: ``` df.merge(s) AttributeError: 'Series' object has no attribute 'columns' ``` and ``` df.join(s) ValueError: Other Series must have a name ``` EDIT The first two answers posted highlighted a problem with my question, so please use the following to construct `df`: ``` df = pd.DataFrame({'a':[np.nan, 2, 3], 'b':[4, 5, 6]}, index=[3, 5, 6]) ``` with the final result ``` a b s1 s2 3 NaN 4 5 6 5 2 5 5 6 6 3 6 5 6 ```